The present invention is directed to a system for treating a field of interest. More particularly, the present invention is directed to providing a decision support system for agricultural management to enable more accurate treatment of the field of interest.
Each field for growing crops is known to contain several soil types, which may be classified according to relative content of sand, clay and humus. There are several common soil types requiring different specific fertilizer for optimum production. Usually, each field contains various soil types placing different requirements in the different areas.
The most common practice is to fertilize the whole field according to the demand of the poorest soils, or according to the demand of average soils, leading to the fact that many field areas receive more or less fertilizer than optimum. This leads to a loss of excess fertilizer and potential lowering crop yield in the whole area compared to optimum yield levels.
There is a need for economical methods and apparatus to apply fertilizers according to the demand of specific areas in a field.
The prior art discloses methods for fertilizer application based on unification of soil types being determined from IR photography or soil maps and administration of the predetermined rate of fertilizer application for the soil types.
However, even in the determined soil type, for instance, "light loam", the amount of clay and powder-like sand may vary in quite a wide range, not speaking of loam unification. It is also worthy to note the amount of irreversible coupled fertilizer that is inaccessible for plants, will be dependent on the content of salts, clay and powder-like sand and humus in each specific place of the field.
One of the most common practices used in accomplishing agricultural management includes measuring a number of field characteristics and working out some instructions which are provided to an application subsystem to apply material to the field. Typically, these instructions have been based on the long-standing experience of management. One of the main governing principles in forming these instructions is to repeat management procedures which have been used in the past with the best results, under similar conditions.
In recent years, a number of mathematical models have been developed to predict yield outcome for a crop in the field of interest based on quantitative parameters and management procedures. Mathematical expressions enable management to obtain estimated parameter values resulting in the greatest yield, or resulting in minimum cost for an acceptable yield.
However, there are several significant weaknesses of such methods. The first is that the working formulas used in such methods are obtained for an entire field. Therefore, one cannot take into account heterogeneity of soil and crop characteristics within the field. This results in significant differences between predicted and measured yield. Further, the working formulas typically ignore dynamic interaction of the soil, crop, and weather characteristics, during the crop growth period. This also significantly contributes to the overall error of such a method.
Further, while there are current engineering, theoretical and experimental investigations into crop productivity, physical and chemical processes in the soil, and other items related to site specific crop management, and while such techniques make it possible to improve decision making techniques in agricultural management, such a decision making task is still extremely complex and currently requires a great deal of manual calculation and manipulation in order to achieve accuracy. There are virtually no effective systems currently available which take into account soil sample analyses over a wide range of field characteristics, and other things such as historical and future weather and atmospheric characteristics, in determining a valuable program for the application of nutrients, pesticides, irrigation, seeds and other crop management applications.